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Forecasting Models of Electricity Prices 2018

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 44934

Special Issue Editor

Higher Technical School of Industrial Engineering, University of Castilla-La Mancha, Campus Universitario S/N, 13071 Ciudad Real, Spain
Interests: power system operation; power system planning; distributed generation; distribution planning; renewable energy sources; smart grid; distribution reliability; demand-side management; energy storage; electric vehicles; optimization
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Special Issue Information

Dear Colleagues,

“Forecasting Models of Electricity Prices 2018” is a continuation of the previous and successful Special Issue, “Forecasting Models of Electricity Prices”.

The electric power industry has been in transition, from a centralized, towards a deregulated, production scheme since the early 1980s. Previous centralized schemes were based on electricity tariffs that were paid by the customers as a function of the aggregate cost of production. In the new unbundled scheme, price forecasting has become an important tool for electric companies and customers to decide on their production offers and demand bids and for regulators to characterize the degree of competition of the market.

Electricity prices have unique features that are not observed in other markets, such as weekly and daily seasonalities, on-peak vs. off-peak hours, price spikes, etc. The fact that electricity is not easily storable and the requirement of meeting the demand at all times makes the development of forecasting techniques a challenging issue.

This Special Issue will include the most important forecasting techniques applied to the forecasting of electricity prices, such as:

  • Statistical time series models: Auto regression models, GARCH, etc.
  • Fourier and wavelet transform models
  • Fundamental or structural econometric models
  • Regime-switching models: Markov, jump diffusion, etc.
  • Multi-agent and game theoretic equilibrium models: Nash-Cournot, supply function equilibrium, agent-based methods, etc.
  • Artificial intelligence models: Neural networks, fuzzy logic, support vector machines, etc.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of electricity price forecasting.

Prof. Javier Contreras
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Electricity price forecasting
  • Time series
  • Transform models
  • Fundamental models
  • Regime-switching
  • Multi-agent
  • Market equilibrium
  • Artificial intelligence

Published Papers (10 papers)

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Research

13 pages, 4822 KiB  
Article
A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market
by Derek W. Bunn, Angelica Gianfreda and Stefan Kermer
Energies 2018, 11(10), 2658; https://doi.org/10.3390/en11102658 - 05 Oct 2018
Cited by 18 | Viewed by 3559
Abstract
This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which [...] Read more.
This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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20 pages, 5493 KiB  
Article
Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting
by Grzegorz Marcjasz, Tomasz Serafin and Rafał Weron
Energies 2018, 11(9), 2364; https://doi.org/10.3390/en11092364 - 07 Sep 2018
Cited by 33 | Viewed by 3841
Abstract
We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of [...] Read more.
We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to raw or transformed prices. Since the variability of prediction errors across windows of different lengths and across datasets can be substantial, selecting ex-ante one window is risky. Instead, we argue that averaging forecasts across different calibration windows is a robust alternative and introduce a new, well-performing weighting scheme for averaging these forecasts. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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19 pages, 16690 KiB  
Article
The Financial Effect of the Electricity Price Forecasts’ Inaccuracy on a Hydro-Based Generation Company
by Umut Ugurlu, Oktay Tas, Aycan Kaya and Ilkay Oksuz
Energies 2018, 11(8), 2093; https://doi.org/10.3390/en11082093 - 11 Aug 2018
Cited by 14 | Viewed by 3363
Abstract
Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial [...] Read more.
Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important loss of profits to the suppliers. In this paper, the financial effect of inaccurate electricity price forecasts on a hydro-based GenCo is examined. Electricity price forecasts of five individual and four hybrid forecast models and the ex-post actual prices are used to schedule the hydro-based GenCo using Mixed Integer Linear Programming (MILP). The financial effect measures of profit loss, Economic Loss Index (ELI) and Price Forecast Disadvantage Index (PFDI), as well as Mean Absolute Error (MAE) of the models are used for comparison of the data from 24 weeks of the year. According to the results, a hybrid model, 50% Artificial Neural Network (ANN)–50% Long Short Term Memory (LSTM), has the best performance in terms of financial effect. Furthermore, the forecast performance evaluation methods, such as Mean Absolute Error (MAE), are not necessarily coherent with inaccurate electricity price forecasts’ financial effect measures. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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26 pages, 1929 KiB  
Article
Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models
by Bartosz Uniejewski and Rafał Weron
Energies 2018, 11(8), 2039; https://doi.org/10.3390/en11082039 - 06 Aug 2018
Cited by 32 | Viewed by 3663
Abstract
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major [...] Read more.
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conducting an empirical study involving datasets from two major power markets (Nord Pool and PJM Interconnection), three expert models, two multi-parameter regression (called baseline) models and four variance stabilizing transformations combined with the seasonal component approach, we discuss the optimal way of implementing the LASSO. We show that using a complex baseline model with nearly 400 explanatory variables, a well chosen variance stabilizing transformation (asinh or N-PIT), and a procedure that recalibrates the LASSO regularization parameter once or twice a day indeed leads to significant accuracy gains compared to the typically considered EPF models. Moreover, by analyzing the structures of the best LASSO-estimated models, we identify the most important explanatory variables and thus provide guidelines to structuring better performing models. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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17 pages, 3359 KiB  
Article
Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process
by Zoran Gligorić, Svetlana Štrbac Savić, Aleksandra Grujić, Milanka Negovanović and Omer Musić
Energies 2018, 11(7), 1911; https://doi.org/10.3390/en11071911 - 22 Jul 2018
Cited by 7 | Viewed by 3123
Abstract
The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, [...] Read more.
The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the influence of many other factors (weather conditions, fuel costs, power plant operating costs, regulations, etc.). In this paper we try to incorporate this uncertainty in the electricity price forecasting model by applying interval numbers to express the price of electricity, with no intention of exploring influencing factors. This paper represents a hybrid model based on fuzzy C-mean clustering and the interval-valued autoregressive process for forecasting the short-term electricity price. A fuzzy C-mean algorithm was used to create interval time series to be forecasted by the interval autoregressive process. In this way, the efficiency of forecasting is improved because we predict the interval, not the crisp value where the price will be. This approach increases the flexibility of the forecasting model. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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22 pages, 3837 KiB  
Article
Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables
by Yuehjen E. Shao and Yi-Shan Tsai
Energies 2018, 11(7), 1848; https://doi.org/10.3390/en11071848 - 14 Jul 2018
Cited by 3 | Viewed by 2691
Abstract
Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, [...] Read more.
Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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21 pages, 5568 KiB  
Article
Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach
by Javier Pórtoles, Camino González and Javier M. Moguerza
Energies 2018, 11(6), 1588; https://doi.org/10.3390/en11061588 - 17 Jun 2018
Cited by 25 | Viewed by 4409
Abstract
Dynamic Trees are a tree-based machine learning technique specially designed for online environments where data are to be analyzed sequentially as they arrive. Our purpose is to test this methodology for the very first time for Electricity Price Forecasting (EPF) by using data [...] Read more.
Dynamic Trees are a tree-based machine learning technique specially designed for online environments where data are to be analyzed sequentially as they arrive. Our purpose is to test this methodology for the very first time for Electricity Price Forecasting (EPF) by using data from the Iberian market. For benchmarking the results, we will compare them against another tree-based technique, Random Forest, a widely used method that has proven its good results in many fields. The benchmark includes several versions of the Dynamic Trees approach for a very short term EPF (one-hour ahead) and also a short term (one-day ahead) approach but only with the best versions. The numerical results show that Dynamic Trees are an adequate method, both for very short and short term EPF—even improving upon the performance of the Random Forest method. The comparison with other studies for the Iberian market suggests that Dynamic Trees is a proper and promising method for EPF. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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18 pages, 2031 KiB  
Article
Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence
by Simon Pezzutto, Gianluca Grilli, Stefano Zambotti and Stefan Dunjic
Energies 2018, 11(6), 1460; https://doi.org/10.3390/en11061460 - 05 Jun 2018
Cited by 28 | Viewed by 4606
Abstract
The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: [...] Read more.
The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1) a self-developed form of Porter’s five forces analysis (PFFA) determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i) uncertainty of future electricity prices; (ii) regulatory complexity; and (iii) generation overcapacities. Similar conclusions derive from (2) a self-developed form of multiple-criteria decision analysis (MCDA). In this case, we find that the electricity market particularly depends on (i) market liberalization and (ii) the European Union (EU)’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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23 pages, 547 KiB  
Article
Electricity Price Forecasting Using Recurrent Neural Networks
by Umut Ugurlu, Ilkay Oksuz and Oktay Tas
Energies 2018, 11(5), 1255; https://doi.org/10.3390/en11051255 - 14 May 2018
Cited by 189 | Viewed by 11734
Abstract
Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and [...] Read more.
Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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25 pages, 3359 KiB  
Article
Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors
by Claudio Monteiro, Ignacio J. Ramirez-Rosado and L. Alfredo Fernandez-Jimenez
Energies 2018, 11(5), 1074; https://doi.org/10.3390/en11051074 - 26 Apr 2018
Cited by 2 | Viewed by 3050
Abstract
This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, [...] Read more.
This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF) of a Beta distribution for the output variable (hourly price) can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI) and a Loss function Indicator (LI) are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL). Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made. Full article
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
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